{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T00:49:58Z","timestamp":1773708598485,"version":"3.50.1"},"publisher-location":"Cham","reference-count":30,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031194955","type":"print"},{"value":"9783031194962","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-19496-2_2","type":"book-chapter","created":{"date-parts":[[2022,10,22]],"date-time":"2022-10-22T05:03:19Z","timestamp":1666414999000},"page":"18-29","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Impact Evaluation of\u00a0Multimodal Information on\u00a0Sentiment Analysis"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0741-3508","authenticated-orcid":false,"given":"Luis N.","family":"Z\u00fa\u00f1iga-Morales","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4239-7144","authenticated-orcid":false,"given":"Jorge \u00c1ngel","family":"Gonz\u00e1lez-Ordiano","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8476-163X","authenticated-orcid":false,"given":"J.Emilio","family":"Quiroz-Ibarra","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6937-1956","authenticated-orcid":false,"given":"Steven J.","family":"Simske","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,10,23]]},"reference":[{"key":"2_CR1","doi-asserted-by":"publisher","first-page":"204","DOI":"10.1016\/j.inffus.2021.06.003","volume":"76","author":"SA Abdu","year":"2021","unstructured":"Abdu, S.A., Yousef, A.H., Salem, A.: Multimodal video sentiment analysis using deep learning approaches, a survey. Inf. Fusion 76, 204\u2013226 (2021)","journal-title":"Inf. Fusion"},{"key":"2_CR2","doi-asserted-by":"crossref","unstructured":"Broder, A.Z., Glassman, S.C., Manasse, M.S., Zweig, G.: Syntactic clustering of the web. Computer Networks and ISDN Systems 29(8), 1157\u20131166 (1997). https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0169755297000317, papers from the Sixth International World Wide Web Conference","DOI":"10.1016\/S0169-7552(97)00031-7"},{"key":"2_CR3","doi-asserted-by":"publisher","first-page":"335","DOI":"10.1007\/s10579-008-9076-6","volume":"42","author":"C Busso","year":"2008","unstructured":"Busso, C., et al.: IEMOCAP: interactive emotional dyadic motion capture database. Lang. Resour. Eval. 42, 335\u2013359 (2008)","journal-title":"Lang. Resour. Eval."},{"key":"2_CR4","doi-asserted-by":"crossref","unstructured":"Chandrasekaran, G., Nguyen, T.N., D., J.H.: Multimodal sentiment analysis for social media applications: a comprehensive review. WIREs Data Min. Knowl. Discov. 11(5) (2021)","DOI":"10.1002\/widm.1415"},{"key":"2_CR5","unstructured":"Chen, L., Huang, T., Miyasato, T., Nakatsu, R.: Multimodal human emotion\/expression recognition. In: Proceedings Third IEEE International Conference on Automatic Face and Gesture Recognition, pp. 366\u2013371 (1998)"},{"key":"2_CR6","unstructured":"Datcu, D., Rothkrantz, L.J.M.: Semantic audio-visual data fusion for automatic emotion recognition. Euromedia (2008)"},{"issue":"4","key":"2_CR7","first-page":"42","volume":"2","author":"V Ganganwar","year":"2012","unstructured":"Ganganwar, V.: An overview of classification algorithms for imbalanced datasets. Int. J. Emerg. Technol. Adv. Eng. 2(4), 42\u201347 (2012)","journal-title":"Int. J. Emerg. Technol. Adv. Eng."},{"key":"2_CR8","unstructured":"Guibon, G., Ochs, M., Bellot, P.: From emojis to sentiment analysis. In: WACAI 2016. Lab-STICC and ENIB and LITIS, Brest, France (2016). https:\/\/hal-amu.archives-ouvertes.fr\/hal-01529708"},{"key":"2_CR9","unstructured":"Hsu, C.W., Chang, C.C., Lin, C.J.: A practical guide to support vector classication. National Taiwan University, Tech. rep. (2016)"},{"issue":"17","key":"2_CR10","doi-asserted-by":"publisher","first-page":"24103","DOI":"10.1007\/s11042-019-7390-1","volume":"78","author":"A Kumar","year":"2019","unstructured":"Kumar, A., Garg, G.: Sentiment analysis of multimodal twitter data. Multimedia Tool. Appl. 78(17), 24103\u201324119 (2019). https:\/\/doi.org\/10.1007\/s11042-019-7390-1","journal-title":"Multimedia Tool. Appl."},{"issue":"4","key":"2_CR11","doi-asserted-by":"publisher","first-page":"528","DOI":"10.26599\/TST.2019.9010021","volume":"25","author":"B Liu","year":"2020","unstructured":"Liu, B., et al.: Context-aware social media user sentiment analysis. Tsinghua Sci. Technol. 25(4), 528\u2013541 (2020)","journal-title":"Tsinghua Sci. Technol."},{"key":"2_CR12","doi-asserted-by":"crossref","unstructured":"Metallinou, A., Lee, S., Narayanan, S.: Audio-visual emotion recognition using gaussian mixture models for face and voice, pp. 250\u2013257 (2008)","DOI":"10.1109\/ISM.2008.40"},{"key":"2_CR13","doi-asserted-by":"publisher","first-page":"62","DOI":"10.1016\/j.dss.2016.02.013","volume":"85","author":"N Oliveira","year":"2016","unstructured":"Oliveira, N., Cortez, P., Areal, N.: Stock market sentiment lexicon acquisition using microblogging data and statistical measures. Decis. Support Syst. 85, 62\u201373 (2016)","journal-title":"Decis. Support Syst."},{"key":"2_CR14","doi-asserted-by":"crossref","unstructured":"Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up? Sentiment classification using machine learning techniques. In: Proceedings of the 2002 Conference on Empirical Methods in Natural Language Processing (EMNLP 2002), pp. 79\u201386. Association for Computational Linguistics (2002). https:\/\/aclanthology.org\/W02-1011","DOI":"10.3115\/1118693.1118704"},{"key":"2_CR15","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa, F., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825\u20132830 (2011)","journal-title":"J. Mach. Learn. Res."},{"key":"2_CR16","doi-asserted-by":"crossref","unstructured":"Poria, S., Cambria, E., Gelbukh, A.: Deep convolutional neural network textual features and multiple kernel learning for utterance-level multimodal sentiment analysis. Association for Computational Linguistics, pp. 2539\u20132544 (2015). https:\/\/www.aclweb.org\/anthology\/D15-1303","DOI":"10.18653\/v1\/D15-1303"},{"key":"2_CR17","doi-asserted-by":"crossref","unstructured":"Poria, S., Cambria, E., Hazarika, D., Mazumder, N., Zadeh, A., Morency, L.P.: Context-dependent sentiment analysis in user-generated videos. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, pp. 873\u2013883 (2017)","DOI":"10.18653\/v1\/P17-1081"},{"key":"2_CR18","doi-asserted-by":"crossref","unstructured":"Poria, S., Majumder, N., Hazarika, D., Cambria, E., Gelbukh, A., Hussain, A.: Multimodal sentiment analysis: Addressing key issues and setting up the baselines (2018)","DOI":"10.1109\/MIS.2018.2882362"},{"key":"2_CR19","unstructured":"P\u00e9rez-Rosas, V., Mihalcea, R., Morency, L.P.: Utterance-level multimodal sentiment analysis. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics, pp. 973\u2013982 (2013)"},{"key":"2_CR20","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"338","DOI":"10.1007\/978-3-319-46478-7_21","volume-title":"Computer Vision \u2013 ECCV 2016","author":"SS Rajagopalan","year":"2016","unstructured":"Rajagopalan, S.S., Morency, L.-P., Baltrus\u0306aitis, T., Goecke, R.: Extending long short-term memory for multi-view structured learning. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9911, pp. 338\u2013353. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46478-7_21"},{"key":"2_CR21","first-page":"1","volume-title":"Data Mining","author":"A Rajaraman","year":"2011","unstructured":"Rajaraman, A., Ullman, J.D.: Data Mining, pp. 1\u201317. Cambridge University Press, Cambridge (2011)"},{"key":"2_CR22","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection (2015)","DOI":"10.1109\/CVPR.2016.91"},{"key":"2_CR23","unstructured":"Redmon, J., Farhadi, A.: YOLOv3: an incremental improvement (2018)"},{"key":"2_CR24","doi-asserted-by":"crossref","unstructured":"Rodrigues, A.P., et al.: Real-time twitter spam detection and sentiment analysis using machine learning and deep learning techniques. Computat. Intell. Neurosci. (2022)","DOI":"10.1155\/2022\/5211949"},{"key":"2_CR25","unstructured":"Silva, L.D., Miyasato, T., Nakatsu, R.: Facial emotion recognition using multi-modal information, pp. 397\u2013401. IEEE (1997)"},{"key":"2_CR26","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1007\/BF00994018","volume":"20","author":"V Vapnik","year":"1995","unstructured":"Vapnik, V., Cortes, C.: Support-vector networks. Mach. Learn. 20, 273\u2013297 (1995)","journal-title":"Mach. Learn."},{"key":"2_CR27","doi-asserted-by":"publisher","unstructured":"Van der Walt, S., et al.: The Scikit-image contributors: Scikit-image: image processing in Python. PeerJ 2, e453 (2014). https:\/\/doi.org\/10.7717\/peerj.453","DOI":"10.7717\/peerj.453"},{"key":"2_CR28","doi-asserted-by":"crossref","unstructured":"Wiggins, B.E.: The discursive power of memes in digital culture: ideology, semiotics, and intertextuality. Routledge, 1st edn. (2019)","DOI":"10.4324\/9780429492303-1"},{"key":"2_CR29","doi-asserted-by":"publisher","first-page":"46","DOI":"10.1109\/MIS.2013.34","volume":"28","author":"M W\u00f6llmer","year":"2013","unstructured":"W\u00f6llmer, M., et al.: Youtube movie reviews: sentiment analysis in an audio-visual context. IEEE Intell. Syst. 28, 46\u201353 (2013)","journal-title":"IEEE Intell. Syst."},{"key":"2_CR30","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1109\/MIS.2016.94","volume":"31","author":"A Zadeh","year":"2016","unstructured":"Zadeh, A., Zellers, R., Pincus, E., Morency, L.P.: Multimodal sentiment intensity analysis in videos: facial gestures and verbal messages. IEEE Intell. Syst. 31, 82\u201388 (2016)","journal-title":"IEEE Intell. Syst."}],"container-title":["Lecture Notes in Computer Science","Advances in Computational Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-19496-2_2","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,10,22]],"date-time":"2022-10-22T07:09:52Z","timestamp":1666422592000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-19496-2_2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031194955","9783031194962"],"references-count":30,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-19496-2_2","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"23 October 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MICAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Mexican International Conference on Artificial Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Monterrey","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Mexico","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24 October 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 October 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"micai2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.micai.org\/2022\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Easychair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"137","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"63","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"46% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"17 External reviewers","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}